AIMC Topic:
Image Interpretation, Computer-Assisted

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A deep learning nomogram kit for predicting metastatic lymph nodes in rectal cancer.

Cancer medicine Sep 30, 2020
BACKGROUND: Preoperative diagnoses of metastatic lymph nodes (LNs) by the most advanced deep learning technology of Faster Region-based Convolutional Neural Network (Faster R-CNN) have not yet been reported.
Image Interpretation, Computer-Assisted Humans Decision Support Techniques Prognosis Risk Assessment Deep Learning Lymph Nodes Nomograms Lymphatic Metastasis Magnetic Resonance Imaging Predictive Value of Tests Rectal Neoplasms Aged, 80 and over Adult Male Middle Aged Female Reproducibility of Results Risk Factors Aged
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Improving Quantitative Magnetic Resonance Imaging Using Deep Learning.

Seminars in musculoskeletal radiology Sep 29, 2020
Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parame...
Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Musculoskeletal Diseases Image Enhancement Humans Deep Learning Contrast Media
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Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images.

BioMed research international Sep 29, 2020
An increasing number of patients infected with nontuberculous mycobacteria (NTM) are observed worldwide. However, it is challenging to identify NTM lung diseases from pulmonary tuberculosis (PTB) due to considerable overlap in classic manifestations ...
Middle Aged Image Interpretation, Computer-Assisted Aged Humans Bronchiectasis Adult Machine Learning Lung Nontuberculous Mycobacteria Sensitivity and Specificity Tomography, X-Ray Computed Tuberculosis, Pulmonary Male Female Mycobacterium Infections, Nontuberculous
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Rocky road to digital diagnostics: implementation issues and exhilarating experiences.

Journal of clinical pathology Sep 28, 2020
Since 2007, we have gradually been building up infrastructure for digital pathology, starting with a whole slide scanner park to build up a digital archive to streamline doing multidisciplinary meetings, student teaching and research, culminating in ...
Image Interpretation, Computer-Assisted Pathology, Clinical Humans Artificial Intelligence
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Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

Virchows Archiv : an international journal of pathology Sep 26, 2020
In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slide...
Humans Phenotype Genetic Predisposition to Disease Staining and Labeling Predictive Value of Tests Microscopy Reproducibility of Results Biomarkers, Tumor Lymphoma, Large B-Cell, Diffuse Deep Learning Antigens, CD20 Proto-Oncogene Proteins c-myc Translocation, Genetic In Situ Hybridization, Fluorescence Image Interpretation, Computer-Assisted Immunohistochemistry Proof of Concept Study
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Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Sep 25, 2020
OBJECTIVES: Moyamoya disease is a unique cerebrovascular disorder that is characterized by chronic bilateral stenosis of the internal carotid arteries and by the formation of an abnormal vascular network called moyamoya vessels. In this stury, the au...
Moyamoya Disease Intracranial Arteriosclerosis Middle Aged Diagnosis, Computer-Assisted Deep Learning Adolescent Magnetic Resonance Imaging Diagnosis, Differential Predictive Value of Tests Image Interpretation, Computer-Assisted Reproducibility of Results Young Adult Adult Male Humans Retrospective Studies Female
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Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients.

Scientific reports Sep 23, 2020
Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors' radiological phenotype...
Soft Tissue Neoplasms Supervised Machine Learning Progression-Free Survival Image Interpretation, Computer-Assisted Middle Aged Magnetic Resonance Imaging Radiography Sarcoma Prognosis Humans Female Reproducibility of Results Aged, 80 and over Aged Young Adult Adult Male
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Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis.

BioMed research international Sep 23, 2020
Methylation of the O-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glioblastoma patients. In this study, a deep learning pipeline is designed for automatic prediction of M...
Magnetic Resonance Imaging DNA Methylation Glioblastoma Image Interpretation, Computer-Assisted Brain Brain Neoplasms Deep Learning ROC Curve Imaging Genomics Humans DNA Modification Methylases DNA Repair Enzymes Tumor Suppressor Proteins Promoter Regions, Genetic
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Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Sep 18, 2020
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially availab...
Magnetic Resonance Imaging Radiology Image Interpretation, Computer-Assisted Neurology Neuroradiography Humans Artificial Intelligence Tomography, X-Ray Computed Deep Learning
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Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Neuroimaging clinics of North America Sep 18, 2020
Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogene...
Deep Learning Glioma Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Neuroimaging Humans Neural Networks, Computer Brain Brain Neoplasms
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